import numpy as np
# Implemented as Example 1 from https://pytorch.org/tutorials/beginner/pytorch_with_examples.html

# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension

N, D_in, H, D_out = 64, 1000, 100, 10

# Create random input and output data

x = np.random.randn(N, D_in)
y = np.random.randn(N, D_out)

# Randomly initialize weights

w1 = np.random.randn(D_in,H)
w2 = np.random.randn(H,D_out)

# Set learning rate, this is done locally currently, but should probably become an input eventually

learning_rate = 1e-6

# Start main loop, Iterations are set internally, should probably become an input eventually

for t in range(500):
    # Forward pass: compute predicted y (is .dot efficient? is it numpy?)
    h = x.dot(w1)
    h_relu = np.maximum(h,0)
    y_pred = h_relu.dot(w2)
    
    # Compute and print loss
    loss = np.square(y_pred - y).sum()
    if t % 100 == 99:
        print(t, loss)
    
    # Backprop to compute gradients of w1 and w2 with respect to loss
    grad_y_pred = 2.0 * (y_pred - y)
    grad_w2 = h_relu.T.dot(grad_y_pred)
    grad_h_relu = grad_y_pred.dot(w2.T)
    grad_h = grad_h_relu.copy()
    grad_h[h < 0] = 0
    grad_w1 = x.T.dot(grad_h)

    # Update weights
    w1 -= learning_rate * grad_w1
    w2 -= learning_rate * grad_w2